Hello,

I'm trying to make dynamic data stream processing engine which can

 - read data from multiple topics on multiple Kafka cluster
 - apply several steps of data processing logic on data stream (like 
filtering, transformation of data, join, etc)
 - one data source / processing logic can be shared among connected 
processing logic (fan-out)
 - load of entire engine should be distributed on engine cluster
 - sink result stream to Kafka cluster, TCP Server, etc
 - every data stream flow can be dynamically composed and modified
 - each operation unit can be handled separately (one flow operation unit 
connected to the output of another operation unit can change the source of 
source to output of the other operation unit dynamically(without stopping 
processing of data processing))

I think akka stream meets most of the requirements above but load 
distribution as akka streams doesn't support load distribution yet.

So, to meet the requirement (load distribution) also, I'm planning to 
connect each end (output) of akka stream flow to simple actor that can 
communicate with other actors on the cluster.
(most of the cases 3 actors which of the first receives data from other 
actors, the second processes data with akka streams, and the last passing 
result to other actors)
Surely I understand that it'll break end-to-end back-pressure capability of 
whole data stream processing.

But if it's the situation I can bear, is it the acceptable way to go?
Or should I find another framework or solution instead of akka streams as 
it's too dangerous breaking end-to-end back-pressure capability?

Any comments / recommendations would be deeply appreciated!

--
JH

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